The AI Debugging Paradox: When Your AI Assistant Leads You Down a Rabbit Hole

As software engineers, we increasingly rely on AI assistants to help solve complex problems. These tools offer impressive capabilities, but what happens when an AI confidently suggests a flawed approach? This post explores a real-world case where an AI-recommended debugging technique created a phantom performance problem—sending me on a wild goose chase to solve an issue that never existed.

The Initial Problem: Unexplained Latency

It started with a simple observation—one of our microservices was experiencing higher latency than expected when communicating with our prediction service. I noticed some concerning log entries:

I0514 09:09:32.662052       1 latency_prediction_based.go:620] requestID: 132 -  Timing breakdown took, DNS: 4ms, Conn: 500ms, Req: 0ms, Send: 31ms, Read: 0ms, Parse: 0ms, Total: 536ms

With both services running in the same Kubernetes cluster, a 500ms connection overhead seemed excessive. I decided to consult an AI assistant to help diagnose the issue.

Enter the AI Assistant

I pasted my code and logs to the AI, asking, “I want to first understand what connection overhead is and how to solve it.”

The AI assistant explained connection overhead in detail—the time needed for TCP handshakes, TLS negotiation, and session establishment. It confidently analyzed my code and suggested several improvements to my HTTP transport configuration:

  • Increasing MaxIdleConns and MaxIdleConnsPerHost
  • Extending IdleConnTimeout
  • Implementing a more aggressive connection warmup strategy

But most critically, the AI endorsed and expanded upon my existing measurement methodology, which used this function:

func SendRequestWithTiming(
    client *http.Client,
    url string,
    method string,
    reqBody []byte,
    headers map[string]string,
    requestID string,
    timeout time.Duration,
) ([]byte, TimingResult, error) {
    // ... other code ...

    // 1. DNS Resolution
    dnsStart := time.Now()
    hosts, dnsErr := net.LookupHost(hostName)
    result.DNSTime = time.Since(dnsStart)

    // 2. Connection Test - THIS IS THE PROBLEMATIC PART
    result.ConnectionTime = 0
    if len(hosts) > 0 {
        connStart := time.Now()
        // Extract port if included in URL
        port := "80"
        if strings.HasPrefix(url, "https://") {
            port = "443"
        }
        // ... port extraction logic ...

        conn, dialErr := net.DialTimeout("tcp", hosts[0]+":"+port, timeout/2)
        result.ConnectionTime = time.Since(connStart)

        if dialErr != nil {
            // Log error
        } else if conn != nil {
            conn.Close()
        }
    }

    // ... HTTP request code ...
}

The AI analyzed this code and even suggested enhancements, adding instrumentation to check if connections were being reused. I implemented these changes, but the problem persisted—we were still seeing 500ms connection times, even with ConnReuse: true in the logs.

The Rabbit Hole Gets Deeper

Over several days, I spent hours implementing the AI’s suggestions:

  1. I modified the connection pool settings
  2. I implemented more aggressive warmup strategies
  3. I added detailed logging
  4. I increased timeouts
  5. I experimented with HTTP/2 vs HTTP/1.1

Each time, I returned to the AI with my results, and each time it offered new theories and more sophisticated approaches. We discussed:

  • Network path analysis
  • Connection pipelining
  • DNS caching
  • Server-side TCP settings

The AI’s suggestions became increasingly complex, requiring significant code changes. I was spending more and more time chasing a performance issue that seemed stubborn and mysterious.

A Different Approach: Alternative Measurement Method

After days of unproductive optimization attempts, I asked the AI for other ways to measure connection overhead. The AI suggested using Go’s httptrace package:

func SendRequestWithTiming(
    client *http.Client,
    url string,
    method string,
    reqBody []byte,
    headers map[string]string,
    requestID string,
    timeout time.Duration,
) ([]byte, TimingResult, error) {
    // ... initial setup ...
    
    // Variables for httptrace
    var dnsStart, dnsEnd, connectStart, connectEnd, tlsStart, tlsEnd time.Time
    
    // Create HTTP request
    req, reqErr := http.NewRequest(method, url, bytes.NewBuffer(reqBody))
    // ... error handling ...
    
    // Set up HTTP tracing
    trace := &httptrace.ClientTrace{
        DNSStart: func(info httptrace.DNSStartInfo) {
            dnsStart = time.Now()
            // Log DNS start
        },
        DNSDone: func(info httptrace.DNSDoneInfo) {
            dnsEnd = time.Now()
            // Log DNS completion
        },
        ConnectStart: func(network, addr string) {
            connectStart = time.Now()
            // Log connection start
        },
        ConnectDone: func(network, addr string, err error) {
            connectEnd = time.Now()
            // Log connection completion
        },
        GotConn: func(info httptrace.GotConnInfo) {
            // Log connection details
            result.ConnectionReused = info.Reused
        },
        // More trace points...
    }
    
    // Apply trace to request
    ctx := httptrace.WithClientTrace(req.Context(), trace)
    req = req.WithContext(ctx)
    
    // Send request and calculate timings...
}

I implemented this approach, ran the code, and was surprised by the results:

I0514 18:45:12.882154       1 latency_prediction_based.go:646] requestID: 451 -  Timing breakdown took, DNS: 0ms, Conn: 0ms, ConnReuse: true, Req: 0ms, Send: 18ms, Read: 2ms, Parse: 0ms, Total: 21ms

Connection time: 0ms. The 500ms overhead had completely disappeared.

The Realization

I shared these results with the AI, asking why there was such a discrepancy. The AI explained something that should have been obvious earlier:

“The key difference between your old version and your new version is that your original measurement technique was flawed. You were creating a separate TCP connection just for measurement, completely separate from the one that the HTTP client was using.”

It turned out that while I thought I was measuring the connection overhead of my HTTP requests, I was actually measuring the time to establish a new TCP connection that wasn’t even being used for the actual request. Meanwhile, the HTTP client was correctly reusing connections from its pool, resulting in near-zero connection times.

Simply put, I had spent days trying to optimize a non-existent problem.

How AI and I Got Confused Together

Looking back, I realized several factors led to this misdirection:

  1. Uncritical acceptance: The AI examined my flawed measurement code and treated it as valid, even suggesting enhancements rather than questioning its fundamental approach

  2. Plausible problem: The connection overhead issue seemed reasonable enough that neither I nor the AI questioned its existence

  3. Expertise gap: I lacked the specific networking expertise to spot the flaw in the measurement approach, and relied on the AI’s apparent confidence

  4. Reinforcing loop: As I reported back with continued issues, the AI offered increasingly complex solutions, reinforcing the idea that we were solving a real problem

  5. Authoritative tone: The AI’s clear, confident explanations made me trust its analysis more than I should have

Lessons for Working with AI on Technical Problems

This routine debugging exercise taught me several valuable lessons about working with AI assistants:

  1. Scrutinize methodology: Before implementing any optimization, verify that your measurement approach is actually measuring what you think it is—even when an AI endorses it.

  2. Understand AI limitations: AI assistants don’t necessarily recognize conceptual flaws in your approach, especially when those flaws aren’t obvious syntax or logic errors.

  3. Check assumptions regularly: If you’re spending too much time optimizing without seeing results, revisit your fundamental assumptions about the problem itself.

  4. Use multiple measurement methods: Different measurement approaches provide valuable cross-checks. If they give significantly different results, investigate why before proceeding.

  5. Be aware of overconfidence: AI assistants can appear authoritative even when working from incorrect premises. Their confident tone doesn’t always reflect accuracy.

The Technical Root Cause

For those interested in the technical details, the original measurement approach was flawed because:

  1. We were creating a fresh TCP connection using net.DialTimeout() just for measurement
  2. This connection was completely separate from the pooled connections the HTTP client used
  3. While the HTTP client was efficiently reusing connections (0ms connection time), our measurement was repeatedly establishing new connections (500ms)
  4. We were spending time optimizing the connection pool based on measurements from outside the pool

Conclusion: Critical Thinking Still Required

This experience highlights that working with AI on technical problems requires the same critical thinking needed when working with any tool or colleague.

AI assistants are powerful tools that can speed up many aspects of development, but they don’t replace the need for fundamental understanding of what you’re doing. They can’t alert you to conceptual flaws they don’t recognize, and they’ll helpfully assist you in optimizing a flawed approach rather than questioning its validity.

As AI becomes more integrated into our workflows, developing a healthy skepticism becomes increasingly important. Key safeguards include:

  • Verifying that measurement methods actually measure what you need
  • Questioning when an AI expands on potentially flawed approaches
  • Noticing when more complexity isn’t yielding results
  • Maintaining enough technical depth to identify conceptual errors

The issue wasn’t that the AI gave me incorrect information about how HTTP connections work—it understood those concepts correctly. The problem was that neither of us noticed the measurement methodology itself was creating the very overhead we were trying to eliminate.

Sometimes the most valuable debugging skill isn’t finding the solution, but correctly identifying the actual problem in the first place.